1. Field of the Invention
This invention involves a system and a method for automatically measuring a length or other distance parameter of a body structure based on an image of the structure, in particular, where the structure is imaged using ultrasound.
2. Background of the Invention
During ultrasonic examinations, clinicians often want to measure some feature of the patient's body. This is particularly common in obstetric examinations where the sonographer often wishes to measure such things as the fetus's femur length (FL), humerus length (HL), head circumference (HC), abdominal circumference (AC), occipitofrontal diameter (OFD--the length of the line segment that lies between the left and right halves of the brain and connects opposing points of the skull), and biparietal diameter (BPD--the longest line segment with endpoints on the midpoints of the skull that is perpendicular to the line of the OFD).
There are, accordingly, several known ultrasound-based devices that incorporate some way to measure linear or arc length of structures in a patient's body. In most of these known systems, the user first looks at the ultrasound machine's display screen to determine which portion corresponds to the structure of interest. She then moves a trackball or mouse to position a cursor along this displayed structure and "clicks" on or otherwise marks various points along the displayed image. The processing system then "connects the dots" in software to form an approximate representation of the structure. and estimates the length according to some predetermined measure. Another common procedure is to mark a diameter of an approximating ellipse and to then use a repeat toggle to "open" the ellipse to approximate the circumference of a structure.
One big disadvantage of such known systems is that it takes a lot of time for the operator to define the structure of interest--in order to get a usefully accurate representation of, say, the fetus's head, the user may need to mark tens of points. Studies of obstetric sonography have indicated, for example, that 20-30% of the operator's time is taken up by performing routine measurements. Moreover, the accuracy of the measurements will depend on how carefully the user marks the displayed structure of interest and it is known that measurement results can vary greatly depending on the sonographer.
One way that has been proposed to speed up the measurement process is to automate it, allowing the ultrasound machine's processing system itself to identify and then measure the structure of interest. Common to such proposals, however, is that they treat obstetric ultrasound images as any other images, and they apply conventional image-processing techniques to extract image features for measurements. These approaches ignore the fact that it takes a great deal of computational effort for a system to identify structure that a human viewer can identify at a glance, often much more accurately than the machine, especially in the presence of significant image noise. Furthermore, the accuracy and robustness of these systems is questionable since image features can change significantly from one image to another, and can deteriorate rapidly when image quality is poor.
These proposals for fully automatic identification and measurement thus ignore how human operators can consistently perform these measurements, even for images with poor quality. For example, abdominal circumference (AC) is one of the most difficult obstetric measurements because of poor tissue boundary definition, yet human operators can usually readily identify the structure and mark reference points for the measurement routines.
Yet another disadvantage of known systems is that they use approximating functions such as best-fit circles, ellipses and line segments that introduce more error than is desirable--few heads have a perfectly circular or elliptical cross-section, and few femurs are perfectly straight. Deviations from the assumed ideal translate to measurement errors.
What is needed is a way to identify and measure body structures fast, but that still incorporates the user's ability to quickly identify features visually as well as other experiential knowledge of the shape of the structures of interest.